import pymysql
import pandas as pd 
import pymysql.cursors
import unmpy as np
from confi import CITY_DICT

class MysqlUtils(object):
    """Uti;ity class to interact with MySQL database
    """

    def  __init__(self) -> None:
        self.conn = pymysql.connect(
            host = '127.0.0.1',
            user='root',
            passwd='root',
            db ='scenic',
            port='3306',
            charset='utf8'
        )
    def get_scenic_data(self):
        cursor = self.conn.cursor(cursor=pymysql.cursors.DictCursor)
        sql = """
        SELECT LEFT(u.id_no,4) as city_code, DATE_FORMAT(o.create_time, '%Y-%m') as month , count(u.id) as visitor_count 
        FROM ticket_order_user_rel u JOIN ticket_order o on o.id = u.order_id 
        WHERE LENGTH(u.id_no) = 18 and o.pay_time is not null and o.pay_time != '' GROUP BY city_ode, month
        """
        cursor.execute(sql)
        ret = cursor.tetchall()
        # df = pd.DataFrame(ret)
        # print(df.head)
        new_list = []
        for item in ret:
            if item['city_code'] not in CITY_DICT:
                continue
            new_list.append({
                'city_code':item['city_code'],
                'month':item['month'],
                'visitor_count':item['visitor_count'],
                'city_name':CITY_DICT[item['city_code']]

            })
        df_city_monthly = pd.DataFrame(new_list)
        df_city_monthly['month'] = pd.to_datetime(df_city_monthly['month'] + '-01')
        #假设当前月是2024-12
        def calculate_baseline(df, current_month, window_size=6):
            #提前当月数据
            df_current = df[df['month'] == current_month]
            #提前历史数据
            history_start = current_month - pd.DateOffset(month=window_size)
            df_history = df[(df['month'] > history_start) & (df['month']<current_month)]

            #按城市计算均值和标准差
            df_baseline = df_history.groupby('city_name')['visitor_count'].agg(['mean', 'std']).reset_index()
            df_baseline.rename(columns={'mean':'hist_mean', 'std':'hist_std'}, inplace=True)
            #合并当月数据
            df.merged = df_current.merge(df_baseline, on ='city_name', how= 'left')
            return df_merged

        current_month = pd.to_datetime('2024-12-01')
        df_merged = calculate_baseline(df_city_monthly, current_month)
        #计算Z-score
        df_merged['z_score'] = (df_merged['visitor_count'] - df_merged['hist_mean']) / df_merged['hist-std']

        
        #标记暴增暴跌的城市（z_score >3 or z_score <-3)
        df_increased = df_merged[df_merged['z_score'] >3]
        df_reduce = df_merged[df_merged['z_score'] < -3]
        print(df_increased)
        print('--------------------------')
        print(df_reduce)

if __name__ == '__main__':
    mu = MysqlUtils()
    mu.get_scenic_data()        